Characterizing the Relationship Between Generative AI, Student Behavior, and Learning Outcomes in Upper-Level CS Education: A Case Study in an Undergraduate Machine Learning Course
As generative artificial intelligence (genAI) tools become embedded in educational workflows, it is essential to understand how students use such systems for learning beyond introductory programming. This work investigates the relationship between students’ genAI usage and conceptual understanding of mathematical and algorithmic principles in an undergraduate machine learning course with 136 students. We develop and deploy a course-specific, custom-interfaced large language model (LLM), CubBot, to examine 1. how students interact with genAI in an upper-level CS course via an analysis of anonymized chat logs, and 2. how genAI usage relates to students’ conceptual understanding and learning outcomes via a randomized, controlled assessment comparing performance with and without CubBot access. This research contributes to the growing body of work on genAI-supported education by providing one of the first empirical investigations of genAI’s relationship with conceptual learning in an upper-level computer science course.